Article

Accelerating the discovery of biodiversity by detecting “new” species based on machine learning method

2024-01-09

Abstract excerpt

<title>Abstract</title> <p>Background Recently, machine learning (ML) has been widely used in species auto-identification systems for multi-scene applications in biodiversity, while most of the existing ML systems relying on images are limited to identifying the species on which they are trained, and unknown species out of the system are normally incorrectly identified. Results Here, we propose a new workflow s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e237c161-9466-5dc4-985e-0a97ef4fbfb8
DOI: 10.21203/rs.3.rs-3832815/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Accelerating the discovery of biodiversity by detecting “new” species based on machine learning methodDOI 10.21203/rs.3.rs-3832815/v1Read

The DeepFaune initiative: a collaborative effort towards the automatic identification of French fauna in camera-trap imagesSemantic proximity Read

LizardLens: A Two-Stage Deep Learning Pipeline for Detecting and Classifying Similar Species in Visually Complex EnvironmentsSemantic proximity Read

LizardLens: A Two-Stage Deep Learning Pipeline for Detecting and Classifying Similar Species in Visually Complex EnvironmentsSemantic proximity Read

Classifying the Unknown: Identification of Insects by Deep Open-set Bayesian LearningSemantic proximity Read

Integration of Deep-Learning and Species Distribution Models for Classification of Animal Species of the Brazilian FaunaSemantic proximity Read

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networksSemantic proximity Read

Applying DNA barcoding to conservation practice: a case study of endangered birds and large mammals in ChinaSemantic proximity Read

A general deep learning model for bird detection in high resolution airborne imagerySemantic proximity Read

MLens: Advancing Real-Time Detection, Identification and Counting of Pathogenic Microparasites through a Web InterfaceSemantic proximity Read

The MosAICo ecosystem: bridging the taxonomic gap in vector surveillance with real-time entomological artificial intelligenceSemantic proximity Read

Automated Specimen Triage for Dark Taxa: Deep Learning Enables Orientation, Sex Identification, and Anatomical Segmentation from Robotic ImagingSemantic proximity Read

A versatile semiautomated image analysis workflow for time-lapsed camera trap image classificationSemantic proximity Read